From Figures REF  (a) and (b) we observe clear trends.
Varying the lengths,
both Rfa variants achieve consistent decoding speed with
nearly-constant memory overhead.
In contrast, the baseline decodes slower for longer sequences, taking
an increasing amount of memory.
Notably, for 2,048-length sequences,
Rfa decodes around 12\(\times \)  faster than the baseline while using less than 10%
of the memory.
Rfa-\(\arccos \)  slightly outperforms Rfa-Gaussian in terms of speed and memory efficiency.
This is because when using the same \(D\)  (as we do here),
the \(\phi _{\arccos }\)  is half the size of \(\phi _{\text{Gaussian}}\) .
These results suggest that Rfa can be particularly useful
in sequence-to-sequence tasks with longer sequences,
e.g., document-level machine translation [1]}.
